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An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach.

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Related Experiment Video

Updated: Jul 12, 2025

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development
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Optimization model for multi-products multi-periods multi-suppliers raw-material selection and composition, and order

Mohammad Rizka Fadhli1, Saladin Uttunggadewa2, Rieske Hadianti3

  • 1Magister of Computational Sciences Program, Institut Teknologi Bandung, Center for Advance Sciences 4th floor, Jalan Ganesha no. 10, Bandung 40132, Indonesia.

Methodsx
|October 27, 2023
PubMed
Summary

This study presents an optimization model for beverage companies to select and order raw materials, ensuring supplier contracts are met. The Mixed Integer Linear Programming approach effectively balances costs and demand, providing optimal solutions.

Keywords:
Inventory controlMix-integer linear programmingMulti-period multi-product multi criteria raw-material selectionMulti-products, multi-periods, multi-suppliers raw-material selection and composition, and order quantity problem

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Industrial Engineering

Background:

  • Beverage companies face complex challenges in raw material procurement, including supplier selection, composition, and order quantities.
  • Balancing multiple selection criteria and minimum order quantity contracts with fluctuating demand is a significant operational hurdle.

Purpose of the Study:

  • To develop an optimization model for multi-product, multi-period raw material selection, composition, and order quantity decisions.
  • To incorporate supplier-specific criteria and minimum order quantity contracts into a unified objective function.

Main Methods:

  • Formulation of a Mixed Integer Linear Programming (MILP) model.
  • Development of a penalty function to enforce one-year minimum order quantity contracts.
  • Relaxation of the penalty function upon contract fulfillment.

Main Results:

  • The MILP model successfully identifies optimal raw material selection, composition, and order quantities.
  • Numerical experiments demonstrate the model's effectiveness across various demand scenarios and objective functions.
  • The study quantifies the influence of different decision criteria on the optimal procurement strategy.

Conclusions:

  • The proposed optimization model provides an effective solution for complex raw material procurement problems in the beverage industry.
  • The integrated approach ensures compliance with supplier contracts while optimizing operational efficiency.
  • The findings offer valuable insights for improving supply chain management and decision-making in similar industrial contexts.